{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Principles of Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we'll dive into the basic principles of machine learning, and how to\n",
    "utilize them via the Scikit-Learn API.\n",
    "\n",
    "After briefly introducing scikit-learn's *Estimator* object, we'll cover **supervised learning**, including *classification* and *regression* problems, and **unsupervised learning**, including *dimensinoality reduction* and *clustering* problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# use seaborn for plot defaults\n",
    "# this can be safely commented out\n",
    "import seaborn; seaborn.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Scikit-learn Estimator Object\n",
    "\n",
    "Every algorithm is exposed in scikit-learn via an ''Estimator'' object. For instance a linear regression is implemented as so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Estimator parameters**: All the parameters of an estimator can be set when it is instantiated, and have suitable default values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression(normalize=True)\n",
    "print(model.normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Estimated Model parameters**: When data is *fit* with an estimator, parameters are estimated from the data at hand. All the estimated parameters are attributes of the estimator object ending by an underscore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.arange(10)\n",
    "y = 2 * x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[ 1  3  5  7  9 11 13 15 17 19]\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dSyTH1XliyGAaAICfEGAAAAwQ4BlEytxpx1wnqMaacoNpAAB+QoBn0FxbIdcJTj12naA6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\n9yABLeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109d43438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, 'o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]\n",
      " [6]\n",
      " [7]\n",
      " [8]\n",
      " [9]]\n",
      "[ 1  3  5  7  9 11 13 15 17 19]\n"
     ]
    }
   ],
   "source": [
    "# The input data for sklearn is 2D: (samples == 10 x features == 1)\n",
    "X = x[:, np.newaxis]\n",
    "print(X)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model on our data\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# underscore at the end indicates a fit parameter\n",
    "print(model.coef_)\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.944304526105059e-31"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# residual error around fit\n",
    "model.residues_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model found a line with a slope 2 and intercept 1, as we'd expect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning: Classification and Regression\n",
    "\n",
    "In **Supervised Learning**, we have a dataset consisting of both features and labels.\n",
    "The task is to construct an estimator which is able to predict the label of an object\n",
    "given the set of features. A relatively simple example is predicting the species of \n",
    "iris given a set of measurements of its flower. This is a relatively simple task. \n",
    "Some more complicated examples are:\n",
    "\n",
    "- given a multicolor image of an object through a telescope, determine\n",
    "  whether that object is a star, a quasar, or a galaxy.\n",
    "- given a photograph of a person, identify the person in the photo.\n",
    "- given a list of movies a person has watched and their personal rating\n",
    "  of the movie, recommend a list of movies they would like\n",
    "  (So-called *recommender systems*: a famous example is the [Netflix Prize](http://en.wikipedia.org/wiki/Netflix_prize)).\n",
    "\n",
    "What these tasks have in common is that there is one or more unknown\n",
    "quantities associated with the object which needs to be determined from other\n",
    "observed quantities.\n",
    "\n",
    "Supervised learning is further broken down into two categories, **classification** and **regression**.\n",
    "In classification, the label is discrete, while in regression, the label is continuous. For example,\n",
    "in astronomy, the task of determining whether an object is a star, a galaxy, or a quasar is a\n",
    "classification problem: the label is from three distinct categories. On the other hand, we might\n",
    "wish to estimate the age of an object based on such observations: this would be a regression problem,\n",
    "because the label (age) is a continuous quantity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Example\n",
    "K nearest neighbors (kNN) is one of the simplest learning strategies: given a new, unknown observation, look up in your reference database which ones have the closest features and assign the predominant class.\n",
    "\n",
    "Let's try it out on our iris classification problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['versicolor']\n"
     ]
    }
   ],
   "source": [
    "from sklearn import neighbors, datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "# create the model\n",
    "knn = neighbors.KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "# fit the model\n",
    "knn.fit(X, y)\n",
    "\n",
    "# What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?\n",
    "# call the \"predict\" method:\n",
    "result = knn.predict([[3, 5, 4, 2],])\n",
    "\n",
    "print(iris.target_names[result])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also do probabilistic predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0.8,  0.2]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict_proba([[3, 5, 4, 2],])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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JAQEBle6bNGkSmZmZfPXVVzdN4AaDgVdeeYX4+HgUCgX//ve/admyZfn9CxcuZOXKleX7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\n/9ndVFMCJv+4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e0db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fig_code import plot_iris_knn\n",
    "plot_iris_knn()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Exercise\n",
    "\n",
    "Use a different estimator on the same problem: ``sklearn.svm.SVC``.\n",
    "\n",
    "*Note that you don't have to know what it is do use it. We're simply trying out the interface here*\n",
    "\n",
    "*If you finish early, try to create a similar plot as above with the SVC estimator.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['virginica']\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression Example\n",
    "\n",
    "One of the simplest regression problems is fitting a line to data, which we saw above.\n",
    "Scikit-learn also contains more sophisticated regression algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a1f85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create some simple data\n",
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "X = np.random.random(size=(20, 1))\n",
    "y = 3 * X.squeeze() + 2 + np.random.randn(20)\n",
    "\n",
    "plt.plot(X.squeeze(), y, 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As above, we can plot a line of best fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CmSfLsjg3eIFoZ5xms4XkeB8AlcUVU9dsjSDX+66jyF3kcKUiIrkn74NXro1l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\nbKTgFRERsZGCV0RExEb/H7fzoLCx7QE1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a51e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Plot the data and the model prediction\n",
    "X_fit = np.linspace(0, 1, 100)[:, np.newaxis]\n",
    "y_fit = model.predict(X_fit)\n",
    "\n",
    "plt.plot(X.squeeze(), y, 'o')\n",
    "plt.plot(X_fit.squeeze(), y_fit);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn also has some more sophisticated models, which can respond to finer features in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a51e940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit a Random Forest\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "model.fit(X, y)\n",
    "\n",
    "# Plot the data and the model prediction\n",
    "X_fit = np.linspace(0, 1, 100)[:, np.newaxis]\n",
    "y_fit = model.predict(X_fit)\n",
    "\n",
    "plt.plot(X.squeeze(), y, 'o')\n",
    "plt.plot(X_fit.squeeze(), y_fit);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whether either of these is a \"good\" fit or not depends on a number of things; we'll discuss details of how to choose a model later in the tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Exercise\n",
    "\n",
    "Explore the ``RandomForestRegressor`` object using IPython's help features (i.e. put a question mark after the object).\n",
    "What arguments are available to ``RandomForestRegressor``?\n",
    "How does the above plot change if you change these arguments?\n",
    "\n",
    "These class-level arguments are known as *hyperparameters*, and we will discuss later how you to select hyperparameters in the model validation section.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Learning: Dimensionality Reduction and Clustering\n",
    "\n",
    "**Unsupervised Learning** addresses a different sort of problem. Here the data has no labels,\n",
    "and we are interested in finding similarities between the objects in question. In a sense,\n",
    "you can think of unsupervised learning as a means of discovering labels from the data itself.\n",
    "Unsupervised learning comprises tasks such as *dimensionality reduction*, *clustering*, and\n",
    "*density estimation*. For example, in the iris data discussed above, we can used unsupervised\n",
    "methods to determine combinations of the measurements which best display the structure of the\n",
    "data. As we'll see below, such a projection of the data can be used to visualize the\n",
    "four-dimensional dataset in two dimensions. Some more involved unsupervised learning problems are:\n",
    "\n",
    "- given detailed observations of distant galaxies, determine which features or combinations of\n",
    "  features best summarize the information.\n",
    "- given a mixture of two sound sources (for example, a person talking over some music),\n",
    "  separate the two (this is called the [blind source separation](http://en.wikipedia.org/wiki/Blind_signal_separation) problem).\n",
    "- given a video, isolate a moving object and categorize in relation to other moving objects which have been seen.\n",
    "\n",
    "Sometimes the two may even be combined: e.g. Unsupervised learning can be used to find useful\n",
    "features in heterogeneous data, and then these features can be used within a supervised\n",
    "framework."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensionality Reduction: PCA\n",
    "\n",
    "Principle Component Analysis (PCA) is a dimension reduction technique that can find the combinations of variables that explain the most variance.\n",
    "\n",
    "Consider the iris dataset. It cannot be visualized in a single 2D plot, as it has 4 features. We are going to extract 2 combinations of sepal and petal dimensions to visualize it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced dataset shape: (150, 2)\n"
     ]
    }
   ],
   "source": [
    "X, y = iris.data, iris.target\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=0.95)\n",
    "pca.fit(X)\n",
    "X_reduced = pca.transform(X)\n",
    "print(\"Reduced dataset shape:\", X_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meaning of the 2 components:\n",
      "0.362 x sepal length (cm) + -0.082 x sepal width (cm) + 0.857 x petal length (cm) + 0.359 x petal width (cm)\n",
      "-0.657 x sepal length (cm) + -0.730 x sepal width (cm) + 0.176 x petal length (cm) + 0.075 x petal width (cm)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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x4wnoy0y0bTPihlWmqiM8vAXh4S0ASE9PIyFhJRMndcVgMDB37mxGPDATBweH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\nOD0D71+VCPXKLSHtYnKVk7Q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TNFBHfn4xEyf1Z/HieKKiKp4jd+jQk717t5Bw4iSlZUY6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\nUFCLp/FHCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a7a2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as plt\n",
    "plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y,\n",
    "           cmap='RdYlBu')\n",
    "\n",
    "print(\"Meaning of the 2 components:\")\n",
    "for component in pca.components_:\n",
    "    print(\" + \".join(\"%.3f x %s\" % (value, name)\n",
    "                     for value, name in zip(component,\n",
    "                                            iris.feature_names)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering: K-means\n",
    "\n",
    "Clustering groups together observations that are homogeneous with respect to a given criterion, finding ''clusters'' in the data.\n",
    "\n",
    "Note that these clusters will uncover relevent hidden structure of the data only if the criterion used highlights it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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PGAzFq0RmTeeiT/P7fz6A5FTsm9VjzNS3brtqfRDLl//BitU3UTSWWvuff7md\nxo2D8fa+e42Dvzp69Dj/nraci5eScHNvhqKB5Jvw1Tdr+HZWfpIePqwtn3y2hYxMT2xtrjJ0SOsi\nxyweTpKkS5lOW/CXv1Zr+WLx8nJl27YbXLt+GtWso1+/iURHR3LxYiQnTx5l9Ji2ODjYsmL5V/Tt\nOxFbW1trhC9KQY/RI2F06dWX3j5/EfVvFTfRohC44yT7t26lbffupXbO0qSqKr89/xoNDsYBkLnv\nNEucnRhajEfJzsYkoGic85YzMt2JOhFd6CS9fMVOMrL80CjJBV5Pu1Up7U+dO4URXDeQiCPHadig\nD9WrywQ84sFIki5l9vb+HDlyjsaNA7h48RpHIi5iNB4hJ9uBYUMn5HXfnT17gtzcIwTV1NK6TQdq\n1LDcx3p8QmsWL1pD9+6DrdkMUYEoBh1m1LxHlrI1CnaODqV+3m2/LSdi5g8o2Tm4d2/HqLdef+Du\n6b3r1nNyxe+oNnp6vvAsVf38SEtLxRBzKW8bOxSun4otVqwhIX78viESRbGMBXByuE6D+gXn4T52\nLIpZ364lK8tM40ZVmPrvp/PWKYql21pVTeTmZqDT2WM251C7phN/V7VqVapWLdkJSMTDQ5J0KWvV\nqgvHjh1k/vyzODp48Mwz7+et27hxKVpdCllZJtJSc5n0dEu2bDmMq2v+r229XgeYrRC5qKj6PDmB\nrzfvpObuaLI1CinhHWnauk2JnsNoNHIu5iyu7h54eXmRmHCZyP/7kJArmQDcPLOU9TWD6DFqxD2P\nE7F7D7FHjxHSpiVZN9OInDIV/+vZAMw/EsWkFQtwdHQi288Lki2JOgczhhq+xYq/d68uXLp0lV27\n49DpVMaM6oanp2fe+uzsbN7972KSb1rGC5yLT8PPbxED+lsm8hg7tg9HI7/HZKpH6o0IfKoY6Na5\nMc8888wdzydEUUmSLgMNG4bSsGFogdd27lxH+w7O+PoGAPCf/ywgM7MxrVvXY8GCTYwd2wOtVsvi\nRfto2PARK0QtKioHBwcm//ozuzdswN7JiWHt25fogKuUlBt8O+4pvPaeIs3JlmovjMcruA4+V1L5\n8yvFOReunYm553FWzf6O5OnfUiXVyC6Pn0hsVpPmtxI0QLWjcZyIiCA0LIzeH7zNpnc/guspGJqE\n8Og/Xy52OyY8PowJd3lsPCHhMleSbNDfGi6g0Thy8lQCA26tr+Hvxzczp7Bx43aq+o6lbVirYscj\nxJ1Ikrb7DH/uAAAgAElEQVSS7Oxr+PrmlwPt378FM7/cRtt2fri7efDeu6uoVas+DRr0o0qValy/\nfg2j0Yi3t0+5ewRFlD82NjZ06tu3VI698uMvaLD7DBp0kJLLqRlzaPL7PA7V8MA5LgWAJDstNUKb\n3PM4sQtWEJxqBMDvWhYXTp+3XCXfekQtxckWHz9Lr1JIs6aELLHMjZ2YcJlF774PJjNhY0YQULdO\nibfR29sHN9cs0iwdA5hNmfhVLzhTnZubG+Hh/Uv83EL8lSTpUrJ37ybS0k+h1ymkpNjSr9/4vyVX\nO27cSMPV1RGAmLPXGTz4GTIyMvDytCEsLL8G89q1v1Cteg52dgZ27LjBwIFPVdiiFKISSMvMu98N\nYJuejU6np/1n77Bnxrco2Tn49u5C27597nmYv//U9KpejdjGIWi2HiLHzkDQU2Oo7udfYJubN1OY\nN2oiDSIvoaCwcv0OhiycjW+NGiXVOgDs7Ox46cU+fPf9ejKzoH6IG89NHsO1a+kleh4h7keSdClI\nSLiEg8NF+vazdIFdu3aTrVvW0qFD/pdWp079WfbbHJydc8jKMlG1aihOTk44ORUceBIVdYSWrRyp\nW9dyRdGwYQZr16yhc2fpAhfWUbdvd46u3or/9WxMqNxsXZ+qVX2pVq06jcMs977PRkXx1ajH4UYa\nXh2aMfgf/7jtkamA4f1JiJ1NlbRcLrjbEjJmCJ0GD+LmzRTMZjPJyclkZmYWeMZ7x+o1hNxK0GCZ\nNGTXspWEPz+5xNvZNqwFbcNa5C0X55EvIYpKknQpuHTpPI2b5I/m9PBwxpibVGAbjUZDv37jAYiL\ni+PHHz9l/Ya1PD/lzQJXydevJ9G8Rf6AFkdHe3JzE0q5BULcXYsundF+819O/LERjaMDk16YXCCB\nmc1mVjz/Bg2OXAAg48AZltrYE/7ilALH6TdxAocbhBB75CitW7cipKlljvVzkVFsemUqLrEJpARV\npdvH02jQqiUArl5eXNSquJosSToLM7Yut4+oFqKykJ+GpaBWrRB27MgfNHP8eDwe7n533DYqKoIN\nG2Yw/f1wnn66JdOnP43JlP+sZePGLVi2LCJvef36Y9Su3bT0ghcPBVVVmfv2u3zZuT8z+g5l37oN\nD7R/s44dGP3e24x84x/Y29sXWHfjRjJ2sZfzlu3RkBZ950FkTcPCGPz0pLwEDbDjoy+pf+Ya1U16\n6p++yvYPv8xb16ZrV1JG9SDOxsxFnYnYfi3pPXbMA8UuREUiV9KlwNnZBb/qHfll3i4MBg0Ggy/N\nmzcjLu4c3t4+Bbrvli2fxUcfWZ6XdnCwY8ITPZk793vGj38SACcnFxo3Gsi8uZvR6RT8/ZsTEFDT\nWk0TlcTvP87B4aul+JgtV6T7Xn+Puq2a4+Liep8978/V1Y1Mf2+ItCTqbMzYB+bfW755M4XtK1bj\n5OFG+169bhsIqaRmFlxOy8j/t6LwxEfTOTd5IrlGIzVr15GBlKJSkyRdSoKC6hIUZBm9HRt7mi1b\nv6V+fW/27ruKl2cL6te3PJJl0OsKfMk4ONiSkZFW4Fg+Pr707Ckz54iSk3wmBi9z/ufOPf4a8bGx\nuDQpfi+NRqOh14fT2Pzex6jJN3Fr15ThLz8PwNUrV/hp1BPUO3qRJA3MGraBiZ99WOBvwKltM9KO\nnMNRVUjTqDi3Db3tHAGBQcWOU4iKQJJ0GYg+vY3Roy01exs3DmL+LwfyknSDBl35+ed1jBvXg6ys\nbL7+eiWTn/3EmuGKSiLqwEG2ffIVmqxsfLq1Y8DTT+Wt825Yj+uG1bjlWCpnXavpQ0CtWiV27pBm\nTQlZPAcALy8nkpJSAVj3zXc0PHoRBQVnM6Qu3sTpZ05SJzgkb9/R/3qDlT7eJJyOwbVuTUY+OaHE\n4hKiopEkXQZsbQs+LmVjk3/V0KvXADZtMvDiC99hzFV58YX3K82MRcJ60tJS2TDlTULOWGp4X9t3\nki0+3nQaPAiAbiOGsSTxCjEbd6Ha29DphUk4Opb+ACxFVfNGZgNozZBrzC24jaLQf9KTpR6LEBWB\nJOkykJPtSEJCMlWquHHzZgYpKQVnterSpTdduvS2UnSiMoqJjsbzzGXA8lnzyFa5fPAo3ErSAEOe\nnwyl8OjSvbQfN4olv2+j3pmrZGMmuXdLguvXL9MYrOH06RgWLNyAWVUJH9yR+n+rEy7E3UiSLgPd\nug1h+47fyTWeRFVt6dt3XIkdW1VVNm9ejkaTRmYmdOo0RK7EBX6BgWz3dcPrkmV8Q7oGnIL877NX\n0WRmZvLTS69hOhED3u70/c8bBNSte8dtqwcEMPzXb9m5dBkGJ0eeHTe20j9/nJiYyOtvziM1IwCA\niCOL+eSjcQTUKJ33Q1QukqTLgKIodGhfOlfKGzYsoXsPDzw9/TEac/npx58YOHBSqZxLVBxubu40\needVDn7+HUpmNs4dWzDu8UdL5VwLp75LwJIdaFEg6jLLX/03z69YcNftq1SvzpApZXsFb03r1m3n\nZro/f46Ny8iqwcaNu5jwuCRpcX9FStJms5mpU6cSHR2NXq/n3Xffxd8//wO3adMmZs6ciU6nY8iQ\nIQwdOrTEAhYF6fXpeHoG3fq3Dg8PeRxFWLTt15e2/Uqnfvdf5cYnWBL0LWp8AqqqyqNRt1Sp4omq\nXkFRLPf8zaZ0PDxuL2P6089LWbk6ElWFju1qMGXK+LIOVZRDRepn2rBhA0ajkQULFvDKK68wffr0\nvHVGo5Hp06fzww8/MGfOHBYuXMi1a9dKLGBRUGamCVVV85YzMmVaS1G2bGr5k/OX6VQ1Nf0kQf9F\nt24d6dROQTXHoZrP06ZlDgP69yqwTUTEMeb/GktqeiBpGYGs+iOV3//YbKWIRXlSpCvpQ4cO0b59\newAaN25MZGRk3rqzZ8/i7++fV4M6NDSU/fv306tXrzseS9wuKysLvV5fqEk0QkP78uMPi6he3YEr\nSRn4+7UrgwhFZRcdFcWlmBiatGuLq6vbPbcd/tZrzMvKJjvqLIq3G0PffqOMoqwYFEXhzTee5unr\n1zCb1QLzVv/peFQ0ZtUzr0tcUVw4d+7ybduJh0+RknRaWhqOjo55y1qtFrPZjEajIS0trcAkEQ4O\nDqSmpt73mF5elaP+bnHaYTKZmPfLDLy9ITPTiINDLXp0H3jf89Wv/zppaWk4ODiUyBWMvBflhzXa\nMG/6J1x6dzZuaTnMq1+NMYu+oVbIvUcj/+OHz++5vjK8F1C8dtxr3/6PdGbJb7NIz6wGgF6XSM8e\nA0vl/03ei4qlSEna0dGR9PT8Kdv+TNAATk5OBdalp6fj4uJy32P+WeygIvtr0Yai2Lx5BQMH1sHJ\nyVILeefOk0RERFGt2p3rfv9dZmba/Te6j+K2obyoDO2wRhtycnKI+mIO9dNMgJY6xxNYOu0THvv8\nwyIfsyK+F2azmeXL15JyM51+fbvi6elRqu1wcnLnlRe7smjxTswq9OkVSmBgrRI/X0V8L+6kMrSj\nsD8yipSkmzVrxubNm+nduzcRERHU/cvjFkFBQcTFxZGSkoKdnR379+9nwgSpGFQYJlNWXoIGqFXL\nm6NHLhc6SQtRXCaTCe3fi4v8bbky27hpJ6vXHOBY5Ckys3ywdwhkze9f8NnHk0r9yi0srAVhf5ka\nUwgo4sCx7t27YzAYGDFiBNOnT+f1119n1apV/Prrr+j1el577TUmTJjAiBEjCA8Px9vbu6TjrpSq\nVKnFgQNn85Y3bYqmdu16VoxIPGzs7OwwdG9Dxq2BYHGedoQMeTjmLj969DiffL6T4yfd0ejaYDJl\nk2tM4cbNIH6Zv9ba4YmHVJGupBVF4e233y7wWmBgYN6/O3fuTOfOnYsXWSVx7do1kpOvUaNGIHq9\n/p7bNmjQjIMHM/j11yiMRhP16/UtcO9fiLLw5McfsLbJXJITk2jTpQP1W5Tc1d2xA/uJORpJ004d\n8Q+y3iQZOTk5KIpS4G9y9+4jGHPz54F3cq7LzRvHcXZ1QUW902GEKHVSzKQU7dixFkfHRKr7ubL2\n9z9o13YU7u4e99wnNLQdICO0hfVoNBr6PlpyVfH+tPLr2SR/8B0+aUbW+v5E6CdTad65U4mf515U\nVeX9D2axfWcCiqLSvWsgz996Htnf3wfVfBJF4wxAVmYCeoMbrs4xjB4pBYKEdVTuenxWlJWVhaI5\nT7fujQgO9uexx8LYt0+6zMTDK2buUqqm5aJBIehSGoe//6XMY1izZgObtprINQdhNNVk9e832LFj\nDwC9e3elV3c7bA0x2NmcpVmjLCY8WpOZXzxH1apVyjxWIUCupEtNRkY6bm75g8AURcFgKN0CD1ev\nJrF//xpsbbXY2PgQFta9VM8nxAMx/a3QjrnsC+9cvJyEovnLADDFldjYeNq1s0wl+9KLj/PC82YU\nRZGCLKJckCvpUuLm5s6JqBvk5BgBOHw4Fje3wALbXLoUz5q1s9m67TtWrvqBnJycIp/PZDKxa/cC\nxoxtwNBh9Qipl82+fVuK0wQhSpTv4F5cuzVNa7yHLSEj7l0DoDR0bN8CW8PFvGUHuwt06tQmb/mj\nT2bTb8A/6D/wRXbv3lfm8Qnxd3IlXUoURaFfv4ksXrQcrc6Mp0cgTZu2KrDN4cMrGf+o5Rd8dnYO\nvy78ld69xxTpfAkJl2nc2Dvv13/t2r4cPnSyeI0QogQNffVFdjYM4fKpM7RuF0a90GZlHkPdurV5\n/Z/dWbZ8N4oCw4cOwM+vOgDffvcLq9akY7BpjMkEL7/6IyuWBeDpKU+nCOuRJH0XCQkXOXp0O6oK\nzZt3w8Pj9lJ+92NjY0OPHsPuuM5sNuPiqvnLtgbs7Ez3PN6+fZtJS48BwN4ugNatu+atc3V1I+JI\nCqGhlmWjMRejUTpKRPnStlcvsHKF4NatQmndKvS21zduisBg0zBvWWcIYP6CpTw3+e6DxhYvXs3W\n7dHo9Qrjx3ancePKPze2KFuSpO8gKekKUVErGDW6FaqqMufn+bRrNxZnZ9cSO4dGo+FmSv49uZwc\nI5mZd6/Vffp0FN7eV+nToilg6T4/efIowcGNAEv5VRtDXRYu2IOLiw3n47Pp0/vxEotXPLw2/bqY\ns8v/AIOOsGcnUL95c2uHVCpcXXRcuZaJTmeZjz07+wo1/O+edDdu2sm3P0ajYvkBP+3dpXw/uxou\nLiX3PSGEJOk7iIjYwYiRLQFLt/Wo0a1YumQHXbr0K9HzNGzYhzk/r8fBQUNKCvToYenqvtM0f7Hn\nTjFqVM285aZNA5n/y5m8JA3QvHlHzOb25OTk0Ly5bYnGKh5OBzZv4cIbHxFw0zK2YlPUGaquXXDf\nRwkros8+nUrffpNIvu6KajZSP9iWRx7pc9ftD0fkJ2iA5BR3jh6Non37sLIIVzwkJEnfgU5nQ0ZG\nFg4Oll/Uyclp2No6lPh5/PwC8PN7Mm/54KHt3LhxHBsbhatJGh555PG8mbD8/WoREXGaJk0CADh2\n7Dy+vrcXg9BoNNjaSoIWJePsrr1UvZWgAfxirxK5dx8deve2YlSlw2Aw8Mfv33IsMhKD3kBwcN17\njvCu5uuO2XQZjdby3WBjuEHNmgFlFK14WEiSvoMOHXox5+ev6dLVj9xcEzu2X2HQoImles6bN1PI\nzT3F8OGWK/j09ExWrlhGt25DAAgObsiuXZc5ffoQigI6nR/t2jYp1ZiEcAnwJ1Wr4mSyJKsrbnY0\nvc+MWBWZRqOhcaNGnDkTyyef/oROq/DYY4NwcnK+bdsRw/sTG/M1Bw7HotfD8PCW+Pr6Fthm795D\nfDP7d9IzzNSt48K/3noanU6+dkXhyaflDrRaLYMHP82pU1FotToGDRpY6s9MXrmSSM2a+V1nDg52\nKBpjgW3Cwnrk/TsmJpp1637AYNCg01WhXbuepRqfeDj1HDWCOadOE//7NrDRE/zkaNw9PJj1zAuY\n4i6h8avKiOlTcbnPnNMVyblzcfzz9bmkZQagqmYOHv6Yb756DSg4wYaiKLzxxtN3PY7RaOTDT1Zy\nM83S47V7bw5ffzOfyc+OLc3wRSUjSfouNBoNISENyux81av7s3XreoKD/QE4c+YyTk53rnKUlpbG\n+fObGTXaUlP51KmLHDq0k2bN2pZZvOLhoCgK46b9C6blv/b1pOcJXLoDDQrq/rPMN/8fk2bNsF6Q\nJWzlqm2kZQYAoCgaLlyuws6dexgx4u4TjWRmZqLT6QrUAr9+/To3bujR3PqW1WgNJCSm3+UIQtyZ\nJOlywtbWlnr1+jJv7hZsbDRodd60b9fpjtvGxp6mVesaect161Yj4nB0GUUqHnbquYtosPQsKSiY\nYi5YOaKSZWOjRTXnoGj+fNoiCxeX27u7wfIo5Zv/9xlHjqai1ZoYNKABjz82FABPT0+8vYxcTb61\nrSmdmkGVb8CdKF3yIG054ucXQM+ej9KhwxhcnL05duww5juUTqxa1Y+TJxLylpOTU9FqZbYsUTaU\n6t55s0KpqGiq37uu9aEdu5gZPpaveoUzd9p7qGr5nlFq3NjBBPjFk5OdTG5OIu3DdISGNr3jtnPm\nLuXAYUdMaiA5ubVYsOgssbGWWgZarZZ//99oQuokUaP6Ffr1tuXR8eFl2RRRCciVdDljMpn47bev\n6dXbUkJ06dJdDBo0KW+UN1h+oZ87F8iCBXsxGLSk3DDQr994a4UsHjLDpk9jgekt1HMXUfyrMvT9\nt++6bVZWFsufeo26UYkApEecY3UVb/pNfKJQ59q6bDkJkSeo2qg+Hfo/+LzWf/yxhfUbjqHVwpjR\nXWnY8P7zs9va2jLzy9c5fDgCBwd76tW7+z7Xrqej0eQ/TZFrciYu7iKBgZb70HXq1OLTj1964LiF\n+JMk6XJm27Y/GDO2MY6Olsk5Ro12Zv269XTsWLBMU/PmHYGOVohQPOzcPT155oevC7Xt5cuXcIy+\nBFh+ZDqYITE6plD7Lvn0C3I/+hn3bJVLtkv5LS6eQc89U+g49+07xOdfHiDX7APAmf8s4ZuZPnh6\n3r/LWafT0aLF/Yu2tA1ryMbNG8k1WXoTvNyv0ry5PHXxIFRV5dvvFhJ5PBEnRw1TnhuBt7eXtcMq\nN6S72wouXbrIuXMxd+z2M+Zm5z2fDeDoaIvRmF2W4QlRYqpUqUpa7fzHktIUFZc6tz/ffyeJa7fg\nnm35G/HIMpOwdvM9tz9wIIJPPv2ZH378FZPJxN69x/MSNEBKahX27DlQhFbcXauWzXhpShuaNUql\nRbObvPfOWBwdne6/o8jz40+LWbgkmZOn3dl3yIU33vrG2iGVK3IlXcZWrfqJ4BA99vYGlixZy8CB\nTxV4brJli47MnfMLY8ZaZuaZO2c3bdsWbdINIazNzs6OAd9MZ+Wb70NaBs5tmhE+bgxmsxmN5t7X\nCKqNvuCyQX+XLWHbtt188PF2jLlVMZluEnXiE1q2CMZsupBXbESnTaZW7ZLvferatR1du7YrkWOZ\nTCZmfDmXszHJuLhoeeWlsZW+zOjJU1fQai0D8xRF4cIllbS0NBwdZZwNyJV0mTp69CAdOnrRunUw\njRoFMW58U7ZtW1NgG1dXd1q2HMH8X87wy7zTtG49CheXyvMMqnj4NGsfxjNL5vLkqoXcuHiJ2aHd\n+KxNDzbOX3jv/SY9ypmqjqRj4kxVR5o9/ehdt123PgJjblUAtFobjkVm0aN7Ozq2N2Oji8He5iwj\nh9UiuG6dkmxaiftixhxWrs3h1BkP9h5w5q1/Vf6rShcXPaqaP0DW2dGEg0PJV3isqORKugzduHGN\nKlXyfxXb29tiMt0+h7SHhyc9egwvy9CEKHW/fTaDwOV7MKCBpCxOvDeD0N49cL1LIZQ2fXoRFNqE\n0xFHadOkET4+dx9Frv3bN5lWm4uNjS1vvfEMJpMJjUZT6gWJHkRU1AkOHT5O89DGBAfXznv9zNlk\ntFpLUSNF0XA+PstaIZaZ558bTWLCDGLOZeLkqOHpp/qUq/fK2iRJl6FmzVqzaNHPjBvXBkVRWL06\nguDgLtYOS4gyYUy6bknQtzglpXI1KemuSRrAx6cKPj3v/YgXwOOP9eNU9PdcueqFTpPKgAHBeTXs\n//pkRHmwdOlavv0hklyzD/N//Y1nnmpO3z6W7wFXF12BCXbcXCv/V7SDgwOfffrPO04sJCRJlylH\nR2datRzOvHnr0WkVgoLa4+cXwMmTkWzcuBxbu5tUr+ZJYmI63bpNwNfXz9ohC1Fo2dnZfPfiPzAd\nPgluTrR/7XmadGift75Gu9ac+3U9Pum5AFxvHIh/jYASOXcNfz++/eZlDh6KwK+6L0FBhRucZg0r\nVh3BpFZDUSDXVJVlyw/kJemXXhzN//17FvEXsnB11fL8lEFWjrbsSIK+M0nSZczT04tePUdx6VI8\nx45tZs/exej1Wbi6GZkyZVjeB/XTT39mzOg3rRytEIWzYuYsjn78DYab6WRipg1ObH7rvzTcnD9t\nY7tH+pKTmUnc+q2o9raMeGUKBoOhxGJ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slJ/5FvdOnvazV7siIiJ1YkkLwsvLC0FBMXjz\nzRL4+nrAdkWC2bwI3t7eqKmxYd8X2+Gl08JDOwhTp8xWOi4REfUClrRAxo+/B8A9kGXZuWhBbe11\nXLz0OdLSWlZ2Ky29iEOHPsfEiVMUTEpERL2Bf4IloJ+uKlReXop77w11bkdEDMN3319UIBUREfU2\nHkkLbtiwEHx18iSCgwcCAKqra6DzNCiciqjnnNi/H59v+itgr0fQtLth+e1yIZbDVJNPP9uH7QX7\nIEkazDSNhSW556+uR65hSQvOaByEc+dGIj//AHQ6Da7XeCMhIVPpWEQ94saNG/ho+RpEllUDAK4f\nOoPdg4yYPd+qcDL1uHDhW2z6n0/R4BgOANj6WimGBRsRF3e3wsnodljSbiA2djqAnr9WLpHSvr1w\nHoFllwG0XAkuoAm4crJU2VAqU1JyGPUNQ/HjdUkk2Ygjx06zpAXF16SJSBjBw+7A9ZGDnNt1GhkB\no0OVC6RC0dGR0GltNz8gf4/Ro+5QLhC1iSVNRMIwGAyIe+6/cXryaJyOGoa6ZQ/jwSWLlY6lKmPG\njMaiBeMwsP959O93HokPBuGB+2coHYt+Bk93q9DRoyWw2S5i1KgohIWFKx2HqFMmzpiOiTP48k5P\nSp5rRvJcs9IxqAN4JK0yH3+8EyNCbEhLD0Wj4yCOHv1S6UhEROQilrTKeGivYMyYYADAlCkRuHbt\nlMKJiIjIVTzdTUSkMg0NDdj6j7dhv9GImaZY3DUhSulI5CIeSauM1DwIp09fAgDs3VuKoKCxCici\not4kSRJWrNyAwncbUfSxJ9b+4V84cOCI0rHIRSxplTGZHsaF84Pxxhvn4OM9CRMmTFY6EhH1ogsX\nzuNUmRc8PFpOlDY47sAHRYcUTkWu4ulugUiShMrKSlRXVyIsLBwGg79Ln2f8+EkAJnVvOCJyC3q9\nAZ7aRue2LMvw5G96t8WHThCfF+9GXe1JGAyeqKy8iurqIQgNNSEsLELpaETkRgYOHIgHzSF491+X\n0CT54Y6hV7HkV08oHYtcxJIWQGXlJfQPvILExBkAgLq6G/jss2M4c2YfS5qIOu3x/7QiwfwNqquv\nIjo6Cj4+PkpHIhexpAVQVXUZY39xcylEg8EPkiTDy4tvGSAi14SFhSEsLEzpGNRFbAEBjB49Fnv3\nfuPcPn68HLKsgYd2UBv3IiIiteORtAD0ej0ixszBtrxPUVNTBbtdQnT0FMTdO1XpaEREpCCWtCCG\nDw/F8OELuv3zXrlSiQMHdsHgr8X1GiA+Pp2vTxERuQmWtMrt378TCxbeAwBwOJrw5hvbYTZnKpyK\niIg6gq9Jq1y/fjcfYp3OE35+koJpiIioM1jSKne9Vnb+u7m5GfYbGgXTEBFRZ/B0t8rdNSEBua/9\nLwwGD3xfI2OmKV3pSERE1EEsaZULDh6O4OBfKx2DiIhcwNPdREREgmJJExERCYolTUREJCiXS7qo\nqAgrV6687b7t27dj7ty5SElJwaeffurqlyAiIurTXHrjWFZWFoqLixEZGXnLPpvNhry8PBQWFqKh\noQFpaWmIi4uDl5dXl8MSERH1JS4dScfExGDdunWQZfmWfcePH0dMTAx0Oh0MBgNCQkJQWlra5aBE\nRER9TZtH0gUFBcjNzW31sezsbJjNZpSUlNz2Pna7Hf7+/s5tvV6Purq6bohKRETUt7RZ0haLBRaL\npVOf0GAwwG63O7ftdjsCAgLavZ/R6N/ubdyBGuZQwwyAOuZQwwwA5xCJGmYA1DNHe7p9MZPo6Gi8\n8MILaGxsRENDA8rLyxEeHt7u/Wy22u6O0uuMRn+3n0MNMwDqmEMNMwCcQyRqmAFQxxwdfZLhcklr\nNBpoNDfXgc7JycGIESNgMpmQmZmJ9PR0SJKEFStW8E1jRERELtDIt3v3lwLc/VkRoJ5nd+4+A6CO\nOdQwA8A5RKKGGQB1zNHRI2kuZkJERCQoljQREZGgeBUsAZ08eQSXL5dBp9Nj2jRzq9f+iYio7+CR\ntGAOHtwL/4BvkJo2GveZDNi16x9KRyIiIoWwpAVTW3cWMTEjAQBBQf4YaGxGc3OzwqmIiEgJLGnB\nOBxSq+2GH5rg4cGHiYioL+Jvf8GEj47D228fwLVr1/HFF6XQ6UL5mjQRUR/FN44JZuTIcAQFDcS+\n4n8jOPheTJkSqnQkIiJSCEtaQP369cfkydOUjkFE1GE1Nd+jtLQM4eGj0L9/kNJxVIMlTUREXfJ5\n8X78edNu1NQGwt/wPpY/MRP3zYhTOpYqsKQFUVFRhtLSj+Hn54Hvv5cRH2+Fr6+v0rGIiNqVt+1T\n1DeEwssLaGgMxLbX97CkuwlLWhBff12EzPmTAQBNTU144/UCmM2ZCqciImqfw9F6u9Fx+9tR5/Hd\n3QJobm5Gv0Ctc9vT0xN6Pd/RTUTuYdKkOyBLNQAAWapFbMwQhROpB4+kBaDVanH1apNz226vR309\nHxoicg/LHk3H0CHv4/TpSxg1KhRzk8xKR1INNoEg7rk7CbmvvQe9QQt7nSfuvz9V6UhERB2W+NBs\npSOoEktaEIMHD4XZvFjpGEREJBC+Jk1ERCQoljQREZGgWNJERESCYkkTEREJiiVNREQkKJY0ERGR\noFjSREREgmJJuzlZltHU1NT+DYmIyO1wMRM3dujwXnx37d/QG3SovNyEhITF8PLyUjoWERF1E5a0\nm7Lb7aiv/xqpaS1XzmpoaMTbBYV44AEuJ0pEpBYsaTf13XfXEBIS6Nz29vaCTicpmIiI1CI3dwf2\nHzwPb28Nfv2rBEREhCsdqc/ia9JuasiQoTh2tMq5/c03ldDreXk4Iuqad/65G6/nX0LZNwNx4usB\nWLv+Tfzwww9Kx+qzeCTtpjw9PTFpUjLy8nbDx0cLnW4QpvzSpHQsInJzJ06cBzQ3z9JVXfHD+fPn\nMWbMGAVT9V0saTc2ePBQzJm9QOkYRKQigwYZ0NxcB63WBwDgb7iBoUN5lk4pPN1NREROixc9grh7\n6qH3rcCAwLP4zdLp8PcPUDpWn8UjaSIictJqtVi/7gmlY9D/45E0ERGRoFjSREREgmJJExERCYol\nTUREJCiX3zhWVFSE999/Hxs3brxlX1ZWFg4fPgy9Xg+NRoPNmzfDYDB0KSgREVFf41JJZ2Vlobi4\nGJGRkbfd/9VXX2Hr1q0IDAy87X4iIiJqn0unu2NiYrBu3TrIsnzLPkmScO7cOTz77LNIS0vDjh07\nuhySiIioL2rzSLqgoAC5ubmtPpadnQ2z2YySkpLb3qe+vh5WqxULFy5EU1MTMjMzMW7cOERERHRf\naiIioj5AI9/ucLgDSkpKkJ+fj02bNrX6uCRJqK+vh16vBwBs2LABY8aMwUMPPdT1tERERH1It684\nVlFRgRUrVmDnzp1obm7GoUOHkJSU1O79bLba7o7S64xGf7efQw0zAOqYQw0zAJxDJGqYAVDHHEaj\nf4du53JJazQaaDQa53ZOTg5GjBgBk8mExMREpKSkwNPTE0lJSRg1apSrX4aIiKjPcvl0d3dz92dF\ngHqe3bn7DIA65lDDDADnEIkaZgDUMUdHj6S5mAkREZGgWNJERESCYkkTEREJiiVNREQkKJY0ERGR\noFjSREREgmJJExERCYolTUREJCiWNBERkaBY0kRERIJiSRMREQmKJU1ERCQoljQREZGgWNJERESC\nYkkTEREJiiVNREQkKJY0ERGRoFjSREREgmJJExERCYolTUREJCiWNBERkaBY0kRERIJiSRMREQmK\nJU1ERCQoljQREZGgWNJERESCYkkTEREJiiVNREQkKJY0ERGRoFjSREREgmJJExERCYolTUREJCiW\nNBERkaBY0kRERIJiSRMREQmKJU1ERCQoz87eoba2Fk899RTsdjscDgdWr16NCRMmtLrN9u3bkZ+f\nD09PTyxbtgwzZszorrxERER9RqdLOicnB3FxccjMzERFRQVWrlyJwsJC536bzYa8vDwUFhaioaEB\naWlpiIuLg5eXV7cGJyIiUrtOl/SCBQuchdvU1ARvb+9W+48fP46YmBjodDrodDqEhISgtLQUUVFR\n3ZOYiIioj2izpAsKCpCbm9vqY9nZ2Rg3bhxsNht+97vf4Zlnnmm13263w9/f37mt1+tRV1fXjZGJ\niIj6hjZL2mKxwGKx3PLx0tJSrFy5EqtWrUJsbGyrfQaDAXa73bltt9sREBDQbhCj0b/d27gDNcyh\nhhkAdcyhhhkAziESNcwAqGeO9nT63d1nzpzBk08+iY0bN2Lq1Km37I+OjsbBgwfR2NiI2tpalJeX\nIzw8vFvCEhER9SUaWZblztzhscceQ2lpKYKDgwEAAQEBePnll5GTk4MRI0bAZDKhoKAA+fn5kCQJ\ny5YtQ3x8fI+EJyIiUrNOlzQRERH1Di5mQkREJCiWNBERkaBY0kRERIJiSRMREQlKqJIuLy9HbGws\nGhsblY7SaTdu3MCyZcuQkZGBhQsXoqqqSulILqmtrcXSpUthtVqRmpqKo0ePKh2pS4qKirBy5Uql\nY3SKJElYs2YNUlNTYbVacf78eaUjdcmxY8dgtVqVjuESh8OBp556CvPmzYPFYsHHH3+sdCSXNDc3\n4+mnn0ZaWhrS09NRVlamdCSXXb16FdOnT0dFRYXSUVz28MMPw2q1wmq14ve//32bt+30sqA9pa6u\nDs8999wty4y6i4KCAkRFReGxxx7Dzp078eqrr96yGps7aG9tdneSlZWF4uJiREZGKh2lUz788EM4\nHA689dZbOHbsGP70pz9h8+bNSsdyyd/+9je8++670Ov1Skdxya5duxAUFIQNGzagpqYGiYmJMJlM\nSsfqtE8++QQeHh548803sX//frzwwgtu+T3lcDiwZs0a+Pr6Kh3FZQ0NDQCAvLy8Dt1eiCNpWZax\nZs0arFixwm1Lev78+Vi6dCkA4OLFi+jXr5/CiVyzYMECpKSkALj92uzuJCYmBuvWrYO7/ZXh4cOH\nnQsFjR8/HidOnFA4ketCQkLw0ksvud1j8KPZs2fjiSeeANByhkOr1SqcyDWzZs3C+vXrAbj376fn\nn38eaWlpMBqNSkdx2alTp1BfX4/Fixdj/vz5OHbsWJu37/Uj6dutBx4cHAyz2YyxY8f2dhyXtLWm\n+fz581FWVoatW7cqlK7jXFmbXUQ/N4fZbEZJSYlCqVxXV1cHg8Hg3NZqtZAkCR4eQjyn7pT7778f\n3377rdIxXObn5weg5TF58sknsXz5coUTuU6r1WL16tUoKirCX/7yF6XjdFphYSGCgoIwZcoUbNmy\nxW2f+Pn6+mLx4sWwWCw4e/YslixZgt27d//8z7csgPj4eDkjI0POyMiQo6Ki5IyMDKUjdUl5ebk8\na9YspWO47NSpU3JCQoK8Z88epaN02ZdffikvX75c6Ridkp2dLb/33nvO7WnTpimYpusuXLggP/LI\nI0rHcNmlS5fkpKQkeceOHUpH6RY2m02+77775Pr6eqWjdMq8efOcPREbGytbLBbZZrMpHavTGhoa\n5B9++MG5nZycLFdWVv7s7YV4TfqDDz5w/ttkMuHvf/+7gmlcs2XLFgwePBiJiYnw8/Nz29NiP67N\n/uKLLyIiIkLpOH1STEwMPvnkE8yZMwdHjx7l46Cg6upqLFq0CGvXrsXkyZOVjuOyd955B1VVVXj0\n0Ufh4+MDjUbjdmdmtm3b5vy31WrF+vXrMXDgQAUTuaawsBClpaVYu3YtqqqqUFdX1+bpeyFK+qc0\nGo3SEVySnJyMVatWYceOHZAkCdnZ2UpHcsmmTZvgcDiQlZUF4Oba7O5Ko9G43fdUfHw8iouLkZqa\nCgBu+730U+72GPzolVdeQW1tLV5++WXnz8Grr77qdu/VmD17NlavXo2MjAw0NTXhmWeegZeXl9Kx\n+qTk5GQ8/fTTmDdvHoCWn++2njBx7W4iIiJBudf5DiIioj6EJU1ERCQoljQREZGgWNJERESCYkkT\nEREJiiVNREQkKJY0ERGRoP4PMR60dYgQS64AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a63d7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "k_means = KMeans(n_clusters=3, random_state=0) # Fixing the RNG in kmeans\n",
    "k_means.fit(X)\n",
    "y_pred = k_means.predict(X)\n",
    "\n",
    "plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y_pred,\n",
    "           cmap='RdYlBu');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recap: Scikit-learn's estimator interface\n",
    "\n",
    "Scikit-learn strives to have a uniform interface across all methods,\n",
    "and we'll see examples of these below. Given a scikit-learn *estimator*\n",
    "object named `model`, the following methods are available:\n",
    "\n",
    "- Available in **all Estimators**\n",
    "  + `model.fit()` : fit training data. For supervised learning applications,\n",
    "    this accepts two arguments: the data `X` and the labels `y` (e.g. `model.fit(X, y)`).\n",
    "    For unsupervised learning applications, this accepts only a single argument,\n",
    "    the data `X` (e.g. `model.fit(X)`).\n",
    "- Available in **supervised estimators**\n",
    "  + `model.predict()` : given a trained model, predict the label of a new set of data.\n",
    "    This method accepts one argument, the new data `X_new` (e.g. `model.predict(X_new)`),\n",
    "    and returns the learned label for each object in the array.\n",
    "  + `model.predict_proba()` : For classification problems, some estimators also provide\n",
    "    this method, which returns the probability that a new observation has each categorical label.\n",
    "    In this case, the label with the highest probability is returned by `model.predict()`.\n",
    "  + `model.score()` : for classification or regression problems, most (all?) estimators implement\n",
    "    a score method.  Scores are between 0 and 1, with a larger score indicating a better fit.\n",
    "- Available in **unsupervised estimators**\n",
    "  + `model.predict()` : predict labels in clustering algorithms.\n",
    "  + `model.transform()` : given an unsupervised model, transform new data into the new basis.\n",
    "    This also accepts one argument `X_new`, and returns the new representation of the data based\n",
    "    on the unsupervised model.\n",
    "  + `model.fit_transform()` : some estimators implement this method,\n",
    "    which more efficiently performs a fit and a transform on the same input data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Validation\n",
    "\n",
    "An important piece of machine learning is **model validation**: that is, determining how well your model will generalize from the training data to future unlabeled data. Let's look at an example using the *nearest neighbor classifier*. This is a very simple classifier: it simply stores all training data, and for any unknown quantity, simply returns the label of the closest training point.\n",
    "\n",
    "With the iris data, it very easily returns the correct prediction for each of the input points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "X, y = iris.data, iris.target\n",
    "clf = KNeighborsClassifier(n_neighbors=1)\n",
    "clf.fit(X, y)\n",
    "y_pred = clf.predict(X)\n",
    "print(np.all(y == y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more useful way to look at the results is to view the **confusion matrix**, or the matrix showing the frequency of inputs and outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[50  0  0]\n",
      " [ 0 50  0]\n",
      " [ 0  0 50]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each class, all 50 training samples are correctly identified. But this **does not mean that our model is perfect!** In particular, such a model generalizes extremely poorly to new data. We can simulate this by splitting our data into a *training set* and a *testing set*. Scikit-learn contains some convenient routines to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12  0  0]\n",
      " [ 0  6  0]\n",
      " [ 0  2 18]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)\n",
    "clf.fit(Xtrain, ytrain)\n",
    "ypred = clf.predict(Xtest)\n",
    "print(confusion_matrix(ytest, ypred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This paints a better picture of the true performance of our classifier: apparently there is some confusion between the second and third species, which we might anticipate given what we've seen of the data above.\n",
    "\n",
    "This is why it's **extremely important** to use a train/test split when evaluating your models.  We'll go into more depth on model evaluation later in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flow Chart: How to Choose your Estimator\n",
    "\n",
    "This is a flow chart created by scikit-learn super-contributor [Andreas Mueller](https://github.com/amueller) which gives a nice summary of which algorithms to choose in various situations. Keep it around as a handy reference!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"http://scikit-learn.org/dev/_static/ml_map.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original source on the [scikit-learn website](http://scikit-learn.org/stable/tutorial/machine_learning_map/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Application: Optical Character Recognition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate the above principles on a more interesting problem, let's consider OCR (Optical Character Recognition) – that is, recognizing hand-written digits.\n",
    "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use scikit-learn's set of pre-formatted digits, which is built-in to the library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and visualizing the digits data\n",
    "\n",
    "We'll use scikit-learn's data access interface and take a look at this data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 8, 8)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "digits = datasets.load_digits()\n",
    "digits.images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot a few of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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s2F2B1rZWVOdXY+uhrZi7Zi4q7tH7utWUos1FWLFrBbp36W46FG1v/OUN9O3a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\nJ+Cv34w+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ea8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8))\n",
    "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(digits.target[i]),\n",
    "            transform=ax.transAxes, color='green')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the data is simply each pixel value within an 8x8 grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 8, 8)\n",
      "[[  0.   0.   5.  13.   9.   1.   0.   0.]\n",
      " [  0.   0.  13.  15.  10.  15.   5.   0.]\n",
      " [  0.   3.  15.   2.   0.  11.   8.   0.]\n",
      " [  0.   4.  12.   0.   0.   8.   8.   0.]\n",
      " [  0.   5.   8.   0.   0.   9.   8.   0.]\n",
      " [  0.   4.  11.   0.   1.  12.   7.   0.]\n",
      " [  0.   2.  14.   5.  10.  12.   0.   0.]\n",
      " [  0.   0.   6.  13.  10.   0.   0.   0.]]\n"
     ]
    }
   ],
   "source": [
    "# The images themselves\n",
    "print(digits.images.shape)\n",
    "print(digits.images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1797, 64)\n",
      "[  0.   0.   5.  13.   9.   1.   0.   0.   0.   0.  13.  15.  10.  15.   5.\n",
      "   0.   0.   3.  15.   2.   0.  11.   8.   0.   0.   4.  12.   0.   0.   8.\n",
      "   8.   0.   0.   5.   8.   0.   0.   9.   8.   0.   0.   4.  11.   0.   1.\n",
      "  12.   7.   0.   0.   2.  14.   5.  10.  12.   0.   0.   0.   0.   6.  13.\n",
      "  10.   0.   0.   0.]\n"
     ]
    }
   ],
   "source": [
    "# The data for use in our algorithms\n",
    "print(digits.data.shape)\n",
    "print(digits.data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 ..., 8 9 8]\n"
     ]
    }
   ],
   "source": [
    "# The target label\n",
    "print(digits.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our data have 1797 samples in 64 dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised Learning: Dimensionality Reduction\n",
    "\n",
    "We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to plot points in 64 dimensions!\n",
    "Instead we'll reduce the dimensions to 2, using an unsupervised method.\n",
    "Here, we'll make use of a manifold learning algorithm called *Isomap*, and transform the data to two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import Isomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iso = Isomap(n_components=2)\n",
    "data_projected = iso.fit_transform(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_projected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakevdp/anaconda/envs/python3.4/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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DdZ+52RSXtr5xpu/5/N7C+T2/83luhlMzZUlERzf0LZfL5PN5stkslUql+ftKpUI+n3/D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\nmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e42c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n",
    "            edgecolor='none', alpha=0.5, cmap=plt.cm.get_cmap('nipy_spectral', 10));\n",
    "plt.colorbar(label='digit label', ticks=range(10))\n",
    "plt.clim(-0.5, 9.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see here that the digits are fairly well-separated in the parameter space; this tells us that a supervised classification algorithm should perform fairly well. Let's give it a try."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification on Digits\n",
    "\n",
    "Let's try a classification task on the digits. The first thing we'll want to do is split the digits into a training and testing sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1347, 64) (450, 64)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
    "                                                random_state=2)\n",
    "print(Xtrain.shape, Xtest.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a simple logistic regression which (despite its confusing name) is a classification algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression(penalty='l2')\n",
    "clf.fit(Xtrain, ytrain)\n",
    "ypred = clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check our classification accuracy by comparing the true values of the test set to the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94666666666666666"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This single number doesn't tell us **where** we've gone wrong: one nice way to do this is to use the *confusion matrix*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[42  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 45  0  1  0  0  0  0  3  1]\n",
      " [ 0  0 47  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 42  0  2  0  3  1  0]\n",
      " [ 0  2  0  0 36  0  0  0  1  1]\n",
      " [ 0  0  0  0  0 52  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 42  0  1  0]\n",
      " [ 0  0  0  0  0  0  0 48  1  0]\n",
      " [ 0  2  0  0  0  0  0  0 38  0]\n",
      " [ 0  0  0  1  0  1  0  1  2 34]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(ytest, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MGTO0YMECPfDAA36vY+KNN95QeXm5ysvLdfPNN2vNmjXKyMjwey1PDRkyRHv27JEknTp1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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e427f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.log(confusion_matrix(ytest, ypred)),\n",
    "           cmap='Blues', interpolation='nearest')\n",
    "plt.grid(False)\n",
    "plt.ylabel('true')\n",
    "plt.xlabel('predicted');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might also take a look at some of the outputs along with their predicted labels. We'll make the bad labels red:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KDWnv83qUrvtVo2w4HHpahdFz0TpABREaBAQBDh7P5XKm3IHP4CJOqCE47XOp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VoNGMzt7e0OJR1W/AozTTuQ6KioXhEm6/yZxz9j385/237tE79mf/r6P7WF5EJHfg3mYlj83SfQ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\nNS0uLgYwDY1Gw0XGGEwm95DFUkQ1qHbeHxNXFhcXXdZwEIMZdg7Rd0yxajQ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VNDSuuqtG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z/OaauqDdUrdnUWu3IPCxDYBTNYCN7unKL/zyIC4XDYXCxL52rpVGt7Na2/zO/S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\ndqQjHelIRzpSg/w/IsDQmH9EdhoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113087ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8))\n",
    "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(Xtest[i].reshape(8, 8), cmap='binary')\n",
    "    ax.text(0.05, 0.05, str(ypred[i]),\n",
    "            transform=ax.transAxes,\n",
    "            color='green' if (ytest[i] == ypred[i]) else 'red')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interesting thing is that even with this simple logistic regression algorithm, many of the mislabeled cases are ones that we ourselves might get wrong!\n",
    "\n",
    "There are many ways to improve this classifier, but we're out of time here. To go further, we could use a more sophisticated model, use cross validation, or apply other techniques.\n",
    "We'll cover some of these topics later in the tutorial."
   ]
  }
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